Article
Business, Finance
Kunliang Xu, Weiqing Wang
Summary: A reliable crude oil price forecast is crucial for market pricing. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms to improve accuracy. The results show that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed, but the rolling EEMD-denoising model has an advantage for long-term forecasting.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Engineering, Environmental
Hyeonseong Yuk, Ho Hyeon Jo, Jihee Nam, Young Uk Kim, Sumin Kim
Summary: This study aims to identify the particles generated during the deterioration of building materials and analyze the composition and concentration of fine dust particles. The results show that the tested building materials produced high levels of PM10 and PM2.5, with carbon, hydrogen, and silicon being the main components.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Megha Chitranshi, Daniel Rui Chen, Peter Kosel, Marc Cahay, Mark Schulz
Summary: A flexible and lightweight carbon nanotube composite air filter for particulate matter removal is proposed. The filter demonstrated high filtration efficiency and hydrophobicity suitable for humid environments.
Article
Green & Sustainable Science & Technology
Jae-ho Choi, Khusniddin Khamraev, Daniel Cheriyan
Summary: Particulate matter (PM) exposure can severely impact human health, and the construction industry has a higher PM footprint in the environment. This study assesses the health risks associated with PM and toxic substances (TSs) generated from construction activities, considering real-time inhalation rate (IR) and PM concentration. The findings highlight the different risk levels of activities on different materials and demonstrate that TS-associated health risk is a more accurate indicator than PM-associated health risk.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Environmental Sciences
Chuqi Guo, Farhana Hasan, Dean Lay, Albert Leo N. Dela Cruz, Ajit Ghimire, Slawo M. Lomnicki
Summary: Phytosampling can serve as a supplementary tool for assessing the chemical composition of PM, providing valuable speciation information different from traditional high-volume PM samplers. It offers advantages such as easy particle recovery, collection under natural conditions, and the ability to accurately represent spatial pollutant distribution. The differences in EPFR and PAH speciation and concentration between Phytosampling and high-volume PM sampling highlight the potential for altered chemical composition in traditional methods.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Environmental Sciences
T. Faria, V Martins, N. Canha, E. Diapouli, M. Manousakas, P. Fetfatzis, M. Gini, S. M. Almeida
Summary: Particulate matter (PM) pollution is a significant environmental concern due to its negative impact on human health. This study evaluates the daily exposure and inhaled dose of PM chemical compounds by integrating the concentrations measured in the micro-environments (MEs) where children spend most of their time. Results indicate that homes and schools contribute the most to children's daily exposure and inhaled dose. The study also highlights the high contribution of indoor sources to the organic fraction of particles, particularly in schools where mineral elements from dust resuspension and chalk usage stand out. Additionally, outdoor activities result in higher inhalation rates, leading to a higher contribution of outdoor particles to the inhaled dose. Overall, this study emphasizes the importance of indoor air quality in relation to children's exposure and health.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Engineering, Environmental
Byeunggon Kim, Yunseon Jang, Juhyeon Kim, Su Kyung Kang, Jungeun Song, Dong-Wook Kim, Seohyeon Jang, Inho Nam, Pyung Soo Lee, Soo-Hwan Jeong
Summary: This study developed a novel transparent filter with high PM removal efficiency and low airflow resistance by combining electrospun nanofiber structures and self-polarizability of tetragonal BaTiO3 nanoparticles. The filter showed high PM removal efficiency and low airflow resistance, with its performance depending on the loading of t-BTO and filter transparency. Further tests demonstrated the durability of the filter under long-term use, maintaining its performance despite slight reduction.
CHEMICAL ENGINEERING JOURNAL
(2022)
Article
Environmental Sciences
S. M. D'Evelyn, C. F. A. Vogel, K. J. Bein, B. Lara, E. A. Laing, R. A. Abarca, Q. Zhang, L. Li, J. Li, T. B. Nguyen, K. E. Pinkerton
Summary: Particulate matter in Imperial Valley is sourced from agriculture, border crossing traffic, Mexicali emissions, and the drying lakebed of the Salton Sea, with dust storms often leading to PM10 exceedances. Research found that coarse PM induces an inflammatory response while fine PM increases the expression of certain genes, attributed not only to particle size but also to chemical composition. Community groups have worked on projects to monitor local air quality, but more information on the composition and toxicity of ambient PM from the region is needed.
ATMOSPHERIC ENVIRONMENT
(2021)
Article
Engineering, Environmental
Jihwan Kim, Taesik Go, Sang Joon Lee
Summary: A novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy and deep learning network. This technique can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions.
JOURNAL OF HAZARDOUS MATERIALS
(2021)
Article
Multidisciplinary Sciences
Yan Xiong, Ziming Zou, Jiatang Cheng
Summary: The cuckoo search algorithm based on cloud model effectively configures the step size factor and adapts to changing optimization problems. Simulation experiments show that this algorithm outperforms other CS and non-CS algorithms.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Green & Sustainable Science & Technology
Jianxun Yang, Shen Qu, Miaomiao Liu, Xingyu Liu, Qi Gao, Wei He, John S. Ji, Jun Bi
Summary: This study found that degraded cityscape caused by particulate matter pollution impedes human stress recovery and causes mental discomfort. Participants viewing clean cityscape photos recovered faster and better than those viewing low-visibility cityscape photos. The results suggest that the gray cityscape reduces well-being by hampering stress recovery.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Environmental Sciences
Ho Hyeon Jo, Jihee Nam, Ji Yong Choi, Taeyeon Kim, Sumin Kim
Summary: The hazardous effects of air pollution, especially the rising levels of 2.5 μm particulate matter (PM), are of great concern. Children are more vulnerable to these effects than adults. This study conducted a case study of architectural and facility renovation in a school, focusing on the exit and entrance gates, and evaluated the influx rate of PM using opening/closing recognition equipment and particle sensors. The results showed that the air curtain reduced the PM influx rate by approximately 8% during the daytime when the door was open, and the reinforcement of the door reduced the fine dust influx rate at night. However, operating the air curtain while the door was closed led to an increase in PM concentration in the corridor. It is necessary to implement both architectural and facility renovation to control the influx of fine dust and provide operating guidelines for school operators.
ENVIRONMENTAL POLLUTION
(2023)
Article
Computer Science, Artificial Intelligence
Malik Braik, Alaa Sheta, Heba Al-Hiary, Sultan Aljahdali
Summary: Modeling of nonlinear industrial systems requires selecting an appropriate model structure and parameter estimation algorithm. In this study, an enhanced version of the Cuckoo Search algorithm was used to estimate parameters for linear and nonlinear models of a winding process. The developed models outperformed other mainstream meta-heuristics and conventional modeling methods in terms of performance.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Environmental Sciences
H. A. Sheikh, B. A. Maher, A. W. Woods, P. Y. Tung, R. J. Harrison
Summary: This study demonstrates the efficacy of roadside green infrastructure (GI) in improving local air quality by capturing and reducing airborne particulate matter (PM). A recently installed 'tredge' was found to be effective in intercepting and depositing PM, resulting in significant reduction in exposure in a school playground setting. The findings suggest that GI can be a cost-effective mitigation strategy.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Engineering, Multidisciplinary
Liangqing Luo, Hongmin Li, Jianzhou Wang, Juncheng Hu
Summary: Wind-speed forecasting is crucial for the efficient utilization of wind energy, but accurate prediction is challenging due to nonlinearity and chaotic characteristics. A machine learning-based forecasting system incorporating advanced optimization algorithms and no negative constraint theory has been proposed, showing superior performance in empirical studies.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Statistics & Probability
Shanshan Qin, Hao Ding, Yuehua Wu, Feng Liu
Summary: This paper introduces a non-negative feature selection/feature grouping method for high-dimensional regression problems and demonstrates its effectiveness through algorithmic solutions and experiments.
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Feng Liu, Guangquan Zhang, Jie Lu
Summary: This article introduces a shared-fuzzy-equivalence-relation neural network (SFERNN) for addressing the multisource heterogeneous UDA problem, which optimizes parameters by minimizing cross-entropy loss and distributional discrepancy between source and target domains. Experimental results demonstrate that SFERNN outperforms existing single-source heterogeneous UDA methods on multiple real-world datasets.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang
Summary: Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain with limited labeled and unlabeled data, by leveraging knowledge from a heterogeneous source domain. Although several methods have been proposed, there is still a lack of theoretical foundation to explain and guide better solutions for the SsHeDA problem.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang
Summary: This study focuses on unsupervised open set domain adaptation, providing learning bounds and a novel algorithm that theoretically investigates the risk of the target classifier on unknown classes to mitigate the discrepancies between source and target domains. By introducing a special term called open set difference, the proposed method outperforms state-of-the-art algorithms in benchmark datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Fan Dong, Jie Lu, Yiliao Song, Feng Liu, Guangquan Zhang
Summary: Concept drift refers to changes in the underlying data distribution of data streams over time. A proposed method based on drift region can effectively identify and utilize the information of drift region to update models.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu
Summary: This article introduces a new method in unsupervised domain adaptation using complementary label data, provides a theoretical bound, and considers two different scenarios. By proposing the complementary label adversarial network CLARINET, the CC-UDA and PC-UDA problems are solved, showing superior performance in experiments.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu
Summary: This article discusses the problem of handling unknown classes in unsupervised open set domain adaptation (UOSDA). A new upper bound risk function is proposed, and a solution is presented for the issue of open set difference in deep neural networks (DNNs). Source-domain risk and epsilon-open set difference are minimized through gradient descent, while distributional discrepancy is minimized using a novel adversarial training strategy. Experimental results show that the proposed method achieves state-of-the-art performance on multiple datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng
Summary: This paper proposes a novel method, minimum-margin (MM) attack, for fast and reliable evaluation of adversarial robustness. Compared with the traditional AutoAttack, our method achieves comparable performance but only costs 3% of the computational time. The reliability of our method lies in evaluating the quality of adversarial examples using the margin, while the computational efficiency is achieved through an effective Sequential TArget Ranking Selection (STARS) method.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162
(2022)
Article
Automation & Control Systems
Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang
Summary: This article proposes a novel framework to address the problem of multiclass classification with imprecise observations (MCIMO). Theoretical analysis based on fuzzy Rademacher complexity is provided, and practical algorithms using support vector machine and neural networks are constructed. Experiments on synthetic and real-world datasets confirm the rationality of the theoretical analysis and the efficacy of the proposed algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu
Summary: This paper introduces a more realistic problem called learning from imprecise observations (LIMO) in multi-class classification, where a classifier is trained with fuzzy observations (fuzzy vectors). By proving the estimation error bound based on the distribution of fuzzy random variables, it is shown that the best classifier can always be learned with infinite fuzzy observations. The experiment results validate the effectiveness of the proposed theory and algorithm.
IEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang
Summary: In this research, a novel algorithm called Auxiliary Open-set Risk (AOSR) is proposed to tackle the open-set learning (OSL) problem, by providing a generalization guarantee and theoretically investigating the risk of the target classifier on unknown classes. The proposed method aims to achieve consistent performance on different training samples drawn from the same distribution.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama
Summary: The Maximum Mean Discrepancy (MMD) test can detect distributional discrepancies between two datasets, but has limitations in detecting adversarial attacks. By improving kernel functions, maximizing test power, and using wild bootstrap, the authors have addressed these limitations and verified the sensitivity of the MMD test to adversarial attacks.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang
Summary: Unsupervised domain adaptation focuses on training a target classifier in the absence of labeled samples from the target domain, with a key challenge being the control of the combined risk. The proposed E-MixNet method utilizes enhanced mixup to calculate a proxy of the combined risk, effectively curbing its increase and improving the performance of existing UDA methods.
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Xiaohui He, Ying Nie, Hengliang Guo, Jianzhou Wang
Article
Environmental Sciences
Muhammad Waqas, Majid Nazeer, Man Sing Wong, Wu Shaolin, Li Hon, Joon Heo
Summary: The socio-economic restriction measures implemented in the United States have significantly reduced nitrogen dioxide (NO2) emissions. The study highlights the impact of factors such as human mobility, population density, income, climate, and stationary sources on the reduction of NO2 at different stations. The research emphasizes the scientific impacts of the NO2 reduction and income inequality revealed by the pandemic on air quality and health disparities.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Guorui Zhi, Jinhong Du, Aizhong Chen, Wenjing Jin, Na Ying, Zhihui Huang, Peng Xu, Di Wang, Jinghua Ma, Yuzhe Zhang, Jiabao Qu, Hao Zhang, Li Yang, Zhanyun Ma, Yanjun Ren, Hongyan Dang, Jianglong Cui, Pengchuan Lin, Zhuoshi He, Jinmin Zhao, Shuo Qi, Weiqi Zhang, Wenjuan Zhao, Yingxin Li, Qian Liu, Chen Zhao, Yi Tang, Peng Wei, Jingxu Wang, Zhen Song, Yao Kong, Xiangzhe Zhu, Yi Shen, Tianning Zhang, Yangxi Chu, Xinmin Zhang, Jiafeng Fu, Qingxian Gao, Jingnan Hu, Zhigang Xue
Summary: An comprehensive emission inventory for China in 2019, which includes both air pollutants and greenhouse gases, was developed in this study. The inventory utilizes existing frameworks and data to provide comparable emissions data and demonstrates the relationship between emissions and economic development.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
I-Ting Ku, Yong Zhou, Arsineh Hecobian, Katherine Benedict, Brent Buck, Emily Lachenmayer, Bryan Terry, Morgan Frazier, Jie Zhang, Da Pan, Lena Low, Amy Sullivan, Jeffrey L. Collett Jr
Summary: Unconventional oil and natural gas development (UOGD) in the United States has expanded rapidly in recent decades, raising concerns about its impact on air quality. This study conducted extensive air monitoring during the development of several large well pads in Broomfield, Colorado, providing a unique opportunity to examine changes in local air toxics and VOC concentrations during well drilling and completions and production. The study identified significant increases in VOC concentrations during drilling operations, highlighting the importance of emissions from synthetic drilling mud. The findings suggest opportunities to mitigate emissions during UOGD operations.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Puji Lestari, Akbar R. Tasrifani, Wiranda I. Suri, Martin J. Wooster, Mark J. Grosvenor, Yusuke Fujii, Vissia Ardiyani, Elisa Carboni, Gareth Thomas
Summary: This study developed field emission factors for various pollutants in peatland fires and estimated the total emissions. Gas samples were collected using an analyzer, while particulate samples were collected using air samplers. The study found significant emissions of CO2, CO, PM2.5, carbon aerosols, water-soluble ions, and elements from the fires in Central Kalimantan, Indonesia in 2019.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Ligang Li, Yuyu Chen, Lu Fan, Dong Sun, Hu He, Yongshou Dai, Yong Wan, Fangfang Chen
Summary: A high-precision retrieval method based on a deep convolutional neural network and satellite remote sensing data is proposed to obtain accurate methane vertical profiles.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Hyung Joo Lee, Toshihiro Kuwayama, Michael Fitzgibbon
Summary: This study investigated the changes in nitrogen dioxide (NO2) air pollution levels and their disparities in California, U.S. during the pandemic of coronavirus disease 2019 (COVID-19). The results showed a decrease in NO2 concentrations, especially in urban and high-traffic areas. However, socially vulnerable populations still experienced higher levels of NO2 exposure. The study suggests that reducing NO2 disparities, particularly racial inequity, can be achieved through continued regulatory actions targeting traffic-related NOx emissions.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Maria Chiara Pietrogrande, Beatrice Biffi, Cristina Colombi, Eleonora Cuccia, Umberto Dal Santo, Luisa Romanato
Summary: This study investigates the chemical composition and oxidative potential of PM10 particles in the Po Valley, Italy, and demonstrates the impact of high levels of atmosphere ammonia. The rural area had significantly higher ammonia concentrations compared to the urban site, resulting in higher levels of secondary inorganic aerosol. Although the SIA components did not contribute significantly to the PM10 oxidative reactivity, they were correlated with the oxidative potential measurements. This suggests that the contribution of SIA to PM oxidative toxicity cannot be ignored.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Natalie Allen, Jan Gacnik, Sarrah M. Dunham-Cheatham, Mae Sexauer Gustin
Summary: Accurate measurement of atmospheric reactive mercury is challenging due to its reactivity and low concentrations. The University of Nevada, Reno Reactive Mercury Active System (RMAS) has been shown to be more accurate than the industry standard, but has limitations including long time resolution and sampling biases. Increasing the sampling flow rate negatively affected RM concentrations, but did not impact the chemical composition of RM captured on membranes.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Chin-Yu Hsu, Wei-Ting Hsu, Ching-Yi Mou, Pei-Yi Wong, Chih-Da Wu, Yu-Cheng Chen
Summary: This study estimated the daily exposure concentrations of PM2.5 for elderly individuals residing in different regions of Taiwan using land use regression with machine learning (LUR_ML) and microenvironmental exposure (ME) models. The accuracy of the models varied across regions, with the ME models exhibiting higher predictions and lower biases. The use of region-specific microenvironmental measurements in the ME model showed potential for accurate prediction of personal PM2.5 exposure.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Xiaohan Si, Kerrie Mengersen, Chuchu Ye, Wenbiao Hu
Summary: This study found that there is an interactive effect between air pollutants and weather factors, which significantly affects influenza transmission. Future research should consider the interactive effects between pollutants and temperature or humidity to evaluate the environment-influenza association.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Luxi Xu, Ruijun Xu, Yunshao Ye, Rui Wang, Jing Wei, Chunxiang Shi, Qiaoxuan Lin, Ziquan Lv, Suli Huang, Qi Tian, Yuewei Liu
Summary: This study aimed to evaluate the impact of ambient air pollution on hospital admissions for angina. The results showed that exposure to ambient particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone are associated with an increased risk of hospital admissions for angina. The association with nitrogen dioxide exposure was found to be the strongest.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Xinyu Yu, Man Sing Wong, Majid Nazeer, Zhengqiang Li, Coco Yin Tung Kwok
Summary: This study proposes a novel method to address the challenge of missing values in satellite-derived AOD products and creates a comprehensive daily AOD dataset for the Guangdong-Hong Kong-Macao Greater Bay Area. By reconstructing missing values and developing a new model, the derived dataset outperforms existing products and agrees well with ground-based observations. Additionally, the dataset exhibits consistent temporal patterns and more spatial details.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Yidan Zhang, Yifan Xu, Bo Peng, Wu Chen, Xiaoyu Cui, Tianle Zhang, Xi Chen, Yuan Yao, Mingjin Wang, Junyi Liu, Mei Zheng, Tong Zhu
Summary: This study developed a sensitive method to measure the metallic components of atmospheric fine particulate matter (PM2.5) and compared the results with different analysis methods. The concentrations of metallic components in personal PM2.5 samples were found to be significantly different from corresponding fixed-site samples. Personal sampling can reduce exposure misclassifications, and measuring metallic components is useful for exploring health risks and identifying sources of PM2.5.
ATMOSPHERIC ENVIRONMENT
(2024)
Review
Environmental Sciences
Jamie Leonard, Lea Ann El Rassi, Mona Abdul Samad, Samantha Prehn, Sanjay K. Mohanty
Summary: Increasing concentrations of microplastics in the Earth's atmosphere could have adverse effects on ecosystems and human health. The deposition rate of airborne microplastics is influenced by both land use and climate, and a global analysis suggests that climate may have a greater impact on the concentration and deposition rate of microplastics than land use.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Tian Zhou, Xiaowen Zhou, Zining Yang, Carmen Cordoba-Jabonero, Yufei Wang, Zhongwei Huang, Pengbo Da, Qiju Luo, Zhijuan Zhang, Jinsen Shi, Jianrong Bi, Hocine Alikhodja
Summary: This study investigated the long-range transport and effects of North African and Middle Eastern dust in East Asia using lidar observations and model simulations. The results showed that the dust originated from multiple sources and had a long transport time. The vertical distribution of the dust was found to be crucial for assessing its impacts.
ATMOSPHERIC ENVIRONMENT
(2024)
